Interest in and needs for provenance are growing as data proliferate. Data are increasing in a wide array of application areas, including scientific workflow systems, logical reasoning systems, text extraction, social media, and linked data. As data volumes expand and as applications become more hybrid and distributed in nature, there Is growing interest in where data came from and how they were produced in order to understand when and how to rely on them. Provenance, or the origin or source of something, can capture a wide range of information. This includes, for example, who or what generated the data, the history of data stewardship, manner of manufacture, place and time of manufacture, and so on.
Annotation is tightly connected with provenance since data are often commented on, described, and referred to. These descriptions or annotations are often critical to the understandability, reusability, and reproducibility of data and thus are often critical components of today’s data and knowledge systems. Provenance has been recognized to be important in a wide range of areas including databases, workflows, knowledge representation and reasoning, and digital libraries. Thus, many disciplines have proposed a wide range of provenance models, techniques, and infrastructure for encoding and using provenance. One timely challenge for the broader community is to understand the range of strengths and weaknesses of different approaches sufficiently to find and use the best models for any given situation. This also comes at a time when a new incubator group has been formed at the World Wide Web Consortium (W3C) to provide a state-of-the-
art understanding and develop a roadmap in the area of provenance for Semantic Web technologies, development, and possible standardization.